SYNTHETIC-1 Release: Two Million Collaboratively Generated Reasoning Traces from Deepseek-R1

SYNTHETIC-1, released from Deepseek-R1, is the largest open reasoning dataset collaboratively generated worldwide, featuring reasoning traces for tasks in math, coding, and science, verified for accuracy by task-specific validators. This dataset includes both correct and incorrect reasoning traces enriched with metadata and offers a curated Supervised Fine-Tuning (SFT) subset with 900k samples, marking it the largest from R1, alongside a preference tuning dataset derived from varying response correctness. The dataset's effectiveness in teaching models reasoning through supervised fine-tuning is demonstrated by the SYNTHETIC-1-SFT-7B model, which significantly enhances reasoning performance. The dataset was built using Genesys, an open-source library facilitating the development and integration of verifiers for synthetic data generation and reinforcement learning, supporting diverse tasks with unique verification methods. The total dataset encompasses 2 million responses, with a post-processed SFT dataset and preference dataset available for further development. Future plans involve a globally distributed collaborative reinforcement learning run, aiming to train a state-of-the-art reasoning model with significant parameter expansion.

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